Werner Benger

The Cactus Code: a problem solving environment for the grid

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. The Cactus Code originated in the academic research community, where it has been developed and used over many years by a large international collaboration of physicists and computational scientists. We discuss how the intensive computing requirements of physics applications now using the Cactus Code encourage the use of distributed and metacomputing, describe the development and experiments which have already been performed with Cactus, and detail how its design makes it an ideal application test-bed for Grid computing.

Cactus Tools for Grid Applications

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. The Cactus Code originated in the academic research community, where it has been developed and used over many years by a large international collaboration of physicists and computational scientists. We discuss here how the intensive computing requirements of physics applications now using the Cactus Code encourage the use of distributed and metacomputing, and detail how its design makes it an ideal application test-bed for Grid computing. We describe the development of tools, and the experiments which have already been performed in a Grid environment with Cactus, including distributed simulations, remote monitoring and steering, and data handling and visualization. Finally, we discuss how Grid portals, such as those already developed for Cactus, will open the door to global computing resources for scientific users.

3D Grazing Collision of Two Black Holes

We present results for two colliding black holes (BHs), with angular momentum, spin, and unequal mass. For the first time, gravitational waveforms are computed for a grazing collision from a full 3D numerical evolution. The collision can be followed through the merger to form a single BH, and through part of the ringdown period of the final BH. The apparent horizon is tracked and studied, and physical parameters, such as the mass of the final BH, are computed. The total energy radiated in gravitational waves is shown to be consistent with the total initial mass of the spacetime and the apparent horizon mass of the final BH.

Unifying biological image formats with HDF5

The biosciences need an image format capable of high performance and long-term maintenance. Is HDF5 the answer?

Cactus Grid Computing: Review of Current Development

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. Here we detail some of the various Grid Tools which have been developed around Cactus, and describe Grid experiments which have been performed to test their application.

VISUALIZING NEURONAL STRUCTURES IN THE HUMAN BRAIN VIA DIFFUSION TENSOR MRI

Acquisition, analysis, and visualization of diffusion tensor magnetic resonance imaging (DT-MRI) is still an evolving technology. This article reviews the fundamentals of the data acquisition process and the pipeline leading to visual results that are interpretable by physicians in their clinical practice. The limitations of common approaches for visualizing the retrieved data are discussed and a new statistical method is presented to assess the reliability of the acquired tensor field. A novel visualization method is proposed which is discussed in light of neurophysiological considerations of the perception of colored patterns. It is argued that this method is more accurate for medical data while providing a nearly optimal visual stimulus. The method is evaluated on a patient study with a brain tumor.

Visualizing Hurricane Katrina: large data management, rendering and display challenges

The onslaught of Hurricane Katrina has highlighted the need for effective information display. Visualization of geoscientific data faces challenges of size, integration and representation. Rendering methods need to cope with the surge of data due to advancements in acquisition techniques and computing power. Moreover, data stemming from different application communities are not compatible a-priori. Holistic representations are important to communicate the causes and impact of natural catastrophes to the scientists themselves, decision-makers and the general public. To address these issues, we have developed efficient data layout mechanisms to ensure fast and uniform access to diverse data. We apply effective rendering techniques that intuitively and interactively convey the phenomena. Finally, we discuss the use of high-resolution displays connected via high-speed networks to support collaboration. These components establish a framework for application in hurricane research, coastal modeling and beyond.

Visualizing Katrina - merging computer simulations with observations

Hurricane Katrina has had a devastating impact on the US Gulf Coast, and her effects will be felt for many years. Forecasts of such events, coupled with timely response, can greatly reduce casualties and save billions of dollars. We show how visualizations from storm surge and atmospheric simulations were used to understand the predictions of how strong, where, and when flooding would occur in the hours leading up to Katrinas landfall. Sophisticated surface, flow and volume visualization techniques show these simulation results interleaved with actual observations, including satellite cloud images, GIS aerial maps and LIDAR showing the 3D terrain of New Orleans. The sheer size and complexity of the data in this application also motivated research in efficient data access mechanisms and rendering algorithms. Our goals were to use the resulting animation as a vehicle for raising awareness in the general populace to the true impact of the event, to create a scientifically accurate representation of the storm and its effects, and to develop a workflow to create similar visualizations for future and simulated hurricanes. Screenings of the animation have been well received, both by the general public and by scientists in the field.

Using the TeraGrid to teach scientific computing

We describe how a new graduate course in scientific computing, taught during Fall 2010 at Louisiana State University, utilized TeraGrid resources to familiarize students with some of the real world issues that computational scientists regularly deal with in their work. The course was designed to provide a broad and practical introduction to scientific computing, creating the basic skills and experience to very quickly get involved in research projects involving modern cyberinfrastructure and complex real world scientific problems. As an integral part of the course, students had to utilize various TeraGrid resources, e.g., by deploying, using and extending scientific software within the national cyberinfrastructure.

A hybrid approach for computing products of high-dimensional geometric algebras

Geometric Algebra is considered as a very intuitive tool to deal with geometric problems and it appears to be increasingly efficient and useful to deal with computer graphics solutions. For example, the Conformal Geometric Algebra includes circles, spheres, planes and lines as algebraic objects, and intersections between these objects are also algebraic objects. More complex objects such as conics, quadric surfaces can also be expressed and be manipulated using an extension of the conformal Geometric Algebra. However due to high dimension of their representations in Geometric Algebra, implementations of Geometric Algebra that are currently available do not allow efficient realizations of these objects. This paper presents a Geometric Algebra implementation dedicated for both low and high dimensions. The proposed method is a hybrid solution for precomputed code with fast execution and runtime computations with low memory requirement. More specifically, the proposed method combines a precomputed table approach with a recursive method using binary trees. Some rules are defined to select the most appropriate choice, according to the dimension of the algebra and the type of multivectors involved in the product. The resulting implementation is well suited for high dimensional spaces (e.g. algebra of dimension 15) as well as for lower dimensional space. This paper details the integration of this hybrid method as a plug-in into Gaalop, which is a very advanced optimizing code generator. This paper also presents some benchmarks to show the performances of our method, especially in high dimensional spaces.